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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
import mmcv
import mmengine
from PIL import Image
from mmocr.utils import dump_ocr_data
def convert_gif(img_path):
"""Convert the gif image to png format.
Args:
img_path (str): The path to the gif image
"""
img = Image.open(img_path)
dst_path = img_path.replace('gif', 'png')
img.save(dst_path)
os.remove(img_path)
print(f'Convert {img_path} to {dst_path}')
def collect_files(img_dir, gt_dir):
"""Collect all images and their corresponding groundtruth files.
Args:
img_dir (str): The image directory
gt_dir (str): The groundtruth directory
Returns:
files (list): The list of tuples (img_file, groundtruth_file)
"""
assert isinstance(img_dir, str)
assert img_dir
assert isinstance(gt_dir, str)
assert gt_dir
ann_list, imgs_list = [], []
for img in os.listdir(img_dir):
img_path = osp.join(img_dir, img)
# mmcv cannot read gif images, so convert them to png
if img.endswith('gif'):
convert_gif(img_path)
img_path = img_path.replace('gif', 'png')
imgs_list.append(img_path)
ann_list.append(osp.join(gt_dir, 'gt_' + img.split('.')[0] + '.txt'))
files = list(zip(sorted(imgs_list), sorted(ann_list)))
assert len(files), f'No images found in {img_dir}'
print(f'Loaded {len(files)} images from {img_dir}')
return files
def collect_annotations(files, nproc=1):
"""Collect the annotation information.
Args:
files (list): The list of tuples (image_file, groundtruth_file)
nproc (int): The number of process to collect annotations
Returns:
images (list): The list of image information dicts
"""
assert isinstance(files, list)
assert isinstance(nproc, int)
if nproc > 1:
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmengine.track_progress(load_img_info, files)
return images
def load_img_info(files):
"""Load the information of one image.
Args:
files (tuple): The tuple of (img_file, groundtruth_file)
Returns:
img_info (dict): The dict of the img and annotation information
"""
assert isinstance(files, tuple)
img_file, gt_file = files
# read imgs while ignoring orientations
img = mmcv.imread(img_file, 'unchanged')
img_info = dict(
file_name=osp.join(osp.basename(img_file)),
height=img.shape[0],
width=img.shape[1],
segm_file=osp.join(osp.basename(gt_file)))
if osp.splitext(gt_file)[1] == '.txt':
img_info = load_txt_info(gt_file, img_info)
else:
raise NotImplementedError
return img_info
def load_txt_info(gt_file, img_info):
"""Collect the annotation information.
The annotation format is as the following:
left, top, right, bottom, "transcription"
Args:
gt_file (str): The path to ground-truth
img_info (dict): The dict of the img and annotation information
Returns:
img_info (dict): The dict of the img and annotation information
"""
anno_info = []
with open(gt_file) as f:
lines = f.readlines()
for line in lines:
xmin, ymin, xmax, ymax = line.split(',')[0:4]
x = max(0, int(xmin))
y = max(0, int(ymin))
w = int(xmax) - x
h = int(ymax) - y
bbox = [x, y, w, h]
segmentation = [x, y, x + w, y, x + w, y + h, x, y + h]
anno = dict(
iscrowd=0,
category_id=1,
bbox=bbox,
area=w * h,
segmentation=[segmentation])
anno_info.append(anno)
img_info.update(anno_info=anno_info)
return img_info
def parse_args():
parser = argparse.ArgumentParser(
description='Generate training and test set of IC11')
parser.add_argument('root_path', help='Root dir path of IC11')
parser.add_argument(
'--nproc', default=1, type=int, help='Number of process')
args = parser.parse_args()
return args
def main():
args = parse_args()
root_path = args.root_path
for split in ['training', 'test']:
print(f'Processing {split} set...')
with mmengine.Timer(print_tmpl='It takes {}s to convert annotation'):
files = collect_files(
osp.join(root_path, 'imgs', split),
osp.join(root_path, 'annotations', split))
image_infos = collect_annotations(files, nproc=args.nproc)
dump_ocr_data(image_infos,
osp.join(root_path, 'instances_' + split + '.json'),
'textdet')
if __name__ == '__main__':
main()
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